The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic–mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1 influenza pandemic and 2003 SARS epidemic.

Building on similarities between earthquakes and extreme financial events, we use a self-organized criticality-generating model to study herding and avalanche dynamics in financial markets. We consider a community of interacting investors, distributed in a small-world network, who bet on the bullish (increasing) or bearish (decreasing) behavior of the market which has been specified according to the S&P 500 historical time series. Remarkably, we find that the size of herding-related avalanches in the community can be strongly reduced by the presence of a relatively small percentage of traders, randomly distributed inside the network, who adopt a random investment strategy. Our findings suggest a promising strategy to limit the size of financial bubbles and crashes. We also obtain that the resulting wealth distribution of all traders corresponds to the well-known Pareto power law, while that of random traders is exponential. In other words, for technical traders, the risk of losses is much greater than the probability of gains compared to those of random traders.

The Institute for Applied Economic Research (Ipea) – a Brazilian think-tank linked to the government – is making a request for proposals for eight IDB consultants to contribute with chapters to a seminal book on Complex Systems applied to Public Policies. On one hand, the project aims at pushing forward the modeling frontier, its methodologies and applications for the case of Brazil. On the other hand, the project pursues actual improvement on the understanding of public policies’ mechanisms and effects, through complex systems’ tools and concepts.The book encompasses five broad themes: (1) concepts and methods; (2) computational tools; (3) public policy phenomena as complex systems (specifically: society, economics, ecology and the cities); (4) applied examples in the world and its emergence in Brazil; and (5) possibilities of prognosis, scenarios and policy-effect analysis using complex systems tools.The consultant is expected to deliver a proposed extended summary, a preliminary version to be discussed in a seminar in Brazil (July-September 2014) and the final version of the chapter.

For a travelling virus, a large metropolitan area on a different continent could be closer than the nearby town. Using measures of connectivity between airports, rather than actual distances, researchers predict where an epidemic will strike next.

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